Navigating a vehicle based on a detected barrier

ABSTRACT

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system for navigating a vehicle includes at least one processing device programmed to receive, from an image capture device, a plurality of images associated with an environment of the vehicle, analyze at least one of the plurality of images to identify a navigable region in the environment of the vehicle, identify, based on the at least one of the plurality of images, at least one barrier associated with an edge of the navigable region, and determine a type of the at least one barrier. The at least one processing device is also programmed to determine a navigational path of the vehicle based on the determined type of the at least one barrier, and cause the vehicle to travel on at least a portion of the determined navigational path.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of application Ser. No. 16/388,937,filed Apr. 19, 2019, now allowed, which is a continuation of applicationSer. No. 15/630,203, filed Jun. 22, 2017, issued as U.S. Pat. No.10,296,010 on May 21, 2019, which claims the benefit of priority of U.S.Provisional Patent Application No. 62/406,604, filed on Oct. 11, 2016.The foregoing applications are incorporated herein by reference in theirentirety.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehiclenavigation. Additionally, this disclosure relates to systems and methodsfor navigating a vehicle based on a detected barrier.

Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Autonomous vehicles may need to take into account a variety of factorsand make appropriate decisions based on those factors to safely andaccurately reach an intended destination. For example, an autonomousvehicle may need to process and interpret visual information (e.g.,information captured from a camera), information from radar or lidar,and may also use information obtained from other sources (e.g., from aGPS device, a speed sensor, an accelerometer, a suspension sensor,etc.). At the same time, in order to navigate to a destination, anautonomous vehicle may also need to identify its location within aparticular roadway (e.g., a specific lane within a multi-lane road),navigate alongside other vehicles, avoid obstacles and pedestrians,observe traffic signals and signs, travel from one road to another roadat appropriate intersections or interchanges, and respond to any othersituation that occurs or develops during the vehicle's operation.Unexpected appearances of obstacles or pedestrians at a relatively shortdistance forward of the vehicle may pose a challenge for the autonomousvehicle. In such case, the autonomous vehicle may need to quicklyanalyze an environment of the vehicle and determine a viable navigablepath for the vehicle to avoid an accident and/or minimize damage.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle. The disclosed systems may provide anavigational response based on, for example, an analysis of imagescaptured by one or more of the cameras. The navigational response mayalso take into account other data including, for example, globalpositioning system (GPS) data, sensor data (e.g., from an accelerometer,a speed sensor, a suspension sensor, etc.), and/or other map data.

In some embodiments, a system for navigating a vehicle may include atleast one processing device programmed to receive, from an image capturedevice, a plurality of images associated with an environment of thevehicle, analyze at least one of the plurality of images to identify anavigable region in the environment of the vehicle, identify, based onthe at least one of the plurality of images, at least one barrierassociated with an edge of the navigable region, and determine a type ofthe at least one barrier. The at least one processing device is alsoprogrammed to determine a navigational path of the vehicle based on thedetermined type of the at least one barrier, and cause the vehicle totravel on at least a portion of the determined navigational path.

In other embodiments, a system for navigating a vehicle includes atleast one processing device programmed to receive, from an image capturedevice, a plurality of images associated with an environment of thevehicle, analyze at least one of the plurality of images to identify anavigable region in the environment of the vehicle, identify, based onthe at least one of the plurality of images, a first barrier associatedwith at least one edge of the navigable region and a second barrierassociated with at least one edge of the navigable region, and determinea type of the first barrier and a type of the second barrier. Thedetermined type of the first barrier includes a traversable barrier andthe determined type of the second barrier includes a non-traversablebarrier. The at least one processing device is also programmed todetermine a navigational path of the vehicle based on the determinedtypes of the first barrier and the second barrier. The determinednavigational path includes traveling through the first barrier in orderto avoid the second barrier. The at least one processing device isfurther programmed to cause the vehicle to travel on at least a portionof the determined navigational path.

In other embodiments, a method for navigating a vehicle includesreceiving, from an image capture device, a plurality of imagesassociated with an environment of the vehicle, analyzing at least one ofthe plurality of images to identify a navigable region in theenvironment of the vehicle, identifying based on the at least one of theplurality of images, at least one barrier associated with an edge of thenavigable region, determining a type of the at least one barrier,determining a navigational path of the vehicle based on the determinedtype of the at least one barrier, and causing the vehicle to travel onat least a portion of the determined navigational path.

In yet other embodiments, a method for navigating a vehicle includesreceiving, from an image capture device, a plurality of imagesassociated with an environment of the vehicle, analyzing at least one ofthe plurality of images to identify a navigable region in theenvironment of the vehicle, identifying, based on the at least one ofthe plurality of images, a first barrier associated with at least oneedge of the navigable region and a second barrier associated with atleast one edge of the navigable region, and determining a type of thefirst barrier and a type of the second barrier. The determined type ofthe first barrier includes a traversable barrier and the determined typeof the second barrier includes a non-traversable barrier. The methodalso includes determining a navigational path of the vehicle based onthe determined types of the first barrier and the second barrier. Thedetermined navigational path includes traveling through the firstbarrier in order to avoid the second barrier. The method furtherincludes causing the vehicle to travel on at least a portion of thedetermined navigational path.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 illustrates a navigation system for a vehicle, consistent withdisclosed embodiments.

FIG. 9 shows an example of a memory storing program modules, consistentwith disclosed embodiments.

FIG. 10A schematically illustrates a bird's eye view of an environmentof an exemplary vehicle consistent with disclosed embodiments.

FIG. 10B schematically illustrates an image of the environment of FIG.10A captured by a forward facing image capture device included in theexemplary vehicle shown in FIG. 10A.

FIG. 11 is a flowchart showing an exemplary process consistent withdisclosed embodiments.

FIG. 12 is a flowchart showing an exemplary process consistent withdisclosed embodiments.

FIG. 13 is a flowchart showing an exemplary process consistent withdisclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Autonomous Vehicle Overview

As used throughout this disclosure, the term “autonomous vehicle” refersto a vehicle capable of implementing at least one navigational changewithout driver input. A “navigational change” refers to a change in oneor more of steering, braking, or acceleration/deceleration of thevehicle. To be autonomous, a vehicle need not be fully automatic (e.g.,fully operational without a driver or without driver input). Rather, anautonomous vehicle includes those that can operate under driver controlduring certain time periods and without driver control during other timeperiods. Autonomous vehicles may also include vehicles that control onlysome aspects of vehicle navigation, such as steering (e.g., to maintaina vehicle course between vehicle lane constraints) or some steeringoperations under certain circumstances (but not under allcircumstances), but may leave other aspects to the driver (e.g., brakingor braking under certain circumstances). In some cases, autonomousvehicles may handle some or all aspects of braking, speed control,and/or steering of the vehicle.

As human drivers typically rely on visual cues and observations in orderto control a vehicle, transportation infrastructures are builtaccordingly, with lane markings, traffic signs, and traffic lightsdesigned to provide visual information to drivers. In view of thesedesign characteristics of transportation infrastructures, an autonomousvehicle may include a camera and a processing unit that analyzes visualinformation captured from the environment of the vehicle. The visualinformation may include, for example, images representing components ofthe transportation infrastructure (e.g., lane markings, traffic signs,traffic lights, etc.) that are observable by drivers and other obstacles(e.g., other vehicles, pedestrians, debris, etc.). Additionally, anautonomous vehicle may also use stored information, such as informationthat provides a model of the vehicle's environment when navigating. Forexample, the vehicle may use GPS data, sensor data (e.g., from anaccelerometer, a speed sensor, a suspension sensor, etc.), and/or othermap data to provide information related to its environment while it istraveling, and the vehicle (as well as other vehicles) may use theinformation to localize itself on the model. Some vehicles can also becapable of communication among them, sharing information, altering thepeer vehicle of hazards or changes in the vehicles' surroundings, etc.

System Overview

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, a user interface 170, and a wirelesstransceiver 172. Processing unit 110 may include one or more processingdevices. In some embodiments, processing unit 110 may include anapplications processor 180, an image processor 190, or any othersuitable processing device. Similarly, image acquisition unit 120 mayinclude any number of image acquisition devices and components dependingon the requirements of a particular application. In some embodiments,image acquisition unit 120 may include one or more image capture devices(e.g., cameras, CCDs, or any other type of image sensor), such as imagecapture device 122, image capture device 124, and image capture device126. System 100 may also include a data interface 128 communicativelyconnecting processing unit 110 to image acquisition unit 120. Forexample, data interface 128 may include any wired and/or wireless linkor links for transmitting image data acquired by image acquisition unit120 to processing unit 110.

Wireless transceiver 172 may include one or more devices configured toexchange transmissions over an air interface to one or more networks(e.g., cellular, the Internet, etc.) by use of a radio frequency,infrared frequency, magnetic field, or an electric field. Wirelesstransceiver 172 may use any known standard to transmit and/or receivedata (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, ZigBee, etc.).Such transmissions can include communications from the host vehicle toone or more remotely located servers. Such transmissions may alsoinclude communications (one-way or two-way) between the host vehicle andone or more target vehicles in an environment of the host vehicle (e.g.,to facilitate coordination of navigation of the host vehicle in view ofor together with target vehicles in the environment of the hostvehicle), or even a broadcast transmission to unspecified recipients ina vicinity of the transmitting vehicle.

Both applications processor 180 and image processor 190 may includevarious types of hardware-based processing devices. For example, eitheror both of applications processor 180 and image processor 190 mayinclude a microprocessor, preprocessors (such as an image preprocessor),graphics processors, a central processing unit (CPU), support circuits,digital signal processors, integrated circuits, memory, or any othertypes of devices suitable for running applications and for imageprocessing and analysis. In some embodiments, applications processor 180and/or image processor 190 may include any type of single or multi-coreprocessor, mobile device microcontroller, central processing unit, etc.Various processing devices may be used, including, for example,processors available from manufacturers such as Intel®, AMD®, etc. andmay include various architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments. In other examples, the EyeQ4® and/or the the EyeQ5® may beused in the disclosed embodiments. Of course, any newer or future EyeQprocessing devices may also be used together with the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. In other embodiments, configuring a processing device mayinclude storing executable instructions on a memory that is accessibleto the processing device during operation. For example, the processingdevice may access the memory to obtain and execute the storedinstructions during operation. In either case, the processing deviceconfigured to perform the sensing, image analysis, and/or navigationalfunctions disclosed herein represents a specialized hardware-basedsystem in control of multiple hardware based components of a hostvehicle.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices. Further, in some embodiments, system 100 may includeone or more of processing unit 110 without including other components,such as image acquisition unit 120.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware, as well as a trained system, such as a neural network, or adeep neural network, for example. The memory units may include randomaccess memory, read only memory, flash memory, disk drives, opticalstorage, tape storage, removable storage and/or any other types ofstorage. In some embodiments, memory units 140, 150 may be separate fromthe applications processor 180 and/or image processor 190. In otherembodiments, these memory units may be integrated into applicationsprocessor 180 and/or image processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

In some embodiments, system 100 may include components such as a speedsensor (e.g., a speedometer) for measuring a speed of vehicle 200.System 100 may also include one or more accelerometers (either singleaxis or multiaxis) for measuring accelerations of vehicle 200 along oneor more axes.

The memory units 140, 150 may include a database, or data organized inany other form, that indication a location of known landmarks. Sensoryinformation (such as images, radar signal, depth information from lidaror stereo processing of two or more images) of the environment may beprocessed together with position information, such as a GPS coordinate,vehicle's ego motion, etc. to determine a current location of thevehicle relative to the known landmarks, and refine the vehiclelocation. Certain aspects of this technology are included in alocalization technology known as REM™, which is being marketed by theassignee of the present application.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.). In some cases, map database 160 may store a sparse datamodel including polynomial representations of certain road features(e.g., lane markings) or target trajectories for the host vehicle. Mapdatabase 160 may also include stored representations of variousrecognized landmarks that may be used to determine or update a knownposition of the host vehicle with respect to a target trajectory. Thelandmark representations may include data fields such as landmark type,landmark location, among other potential identifiers.

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

One or more cameras (e.g., image capture devices 122, 124, and 126) maybe part of a sensing block included on a vehicle. Various other sensorsmay be included in the sensing block, and any or all of the sensors maybe relied upon to develop a sensed navigational state of the vehicle. Inaddition to cameras (forward, sideward, rearward, etc), other sensorssuch as RADAR, LIDAR, and acoustic sensors may be included in thesensing block. Additionally, the sensing block may include one or morecomponents configured to communicate and transmit/receive informationrelating to the environment of the vehicle. For example, such componentsmay include wireless transceivers (RF, etc.) that may receive from asource remotely located with respect to the host vehicle sensor basedinformation or any other type of information relating to the environmentof the host vehicle. Such information may include sensor outputinformation, or related information, received from vehicle systems otherthan the host vehicle. In some embodiments, such information may includeinformation received from a remote computing device, a centralizedserver, etc. Furthermore, the cameras may take on many differentconfigurations: single camera units, multiple cameras, camera clusters,long FOV, short FOV, wide angle, fisheye, etc.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light fixtures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

As discussed earlier, wireless transceiver 172 may and/or receive dataover one or more networks (e.g., cellular networks, the Internet, etc.).For example, wireless transceiver 172 may upload data collected bysystem 100 to one or more servers, and download data from the one ormore servers. Via wireless transceiver 172, system 100 may receive, forexample, periodic or on demand updates to data stored in map database160, memory 140, and/or memory 150. Similarly, wireless transceiver 172may upload any data (e.g., images captured by image acquisition unit120, data received by position sensor 130 or other sensors, vehiclecontrol systems, etc.) from system 100 and/or any data processed byprocessing unit 110 to the one or more servers.

System 100 may upload data to a server (e.g., to the cloud) based on aprivacy level setting. For example, system 100 may implement privacylevel settings to regulate or limit the types of data (includingmetadata) sent to the server that may uniquely identify a vehicle and ordriver/owner of a vehicle. Such settings may be set by user via, forexample, wireless transceiver 172, be initialized by factory defaultsettings, or by data received by wireless transceiver 172.

In some embodiments, system 100 may upload data according to a “high”privacy level, and under setting a setting, system 100 may transmit data(e.g., location information related to a route, captured images, etc.)without any details about the specific vehicle and/or driver/owner. Forexample, when uploading data according to a “high” privacy setting,system 100 may not include a vehicle identification number (VIN) or aname of a driver or owner of the vehicle, and may instead transmit data,such as captured images and/or limited location information related to aroute.

Other privacy levels are contemplated as well. For example, system 100may transmit data to a server according to an “intermediate” privacylevel and include additional information not included under a “high”privacy level, such as a make and/or model of a vehicle and/or a vehicletype (e.g., a passenger vehicle, sport utility vehicle, truck, etc.). Insome embodiments, system 100 may upload data according to a “low”privacy level. Under a “low” privacy level setting, system 100 mayupload data and include information sufficient to uniquely identify aspecific vehicle, owner/driver, and/or a portion or entirely of a routetraveled by the vehicle. Such “low” privacy level data may include oneor more of, for example, a VIN, a driver/owner name, an originationpoint of a vehicle prior to departure, an intended destination of thevehicle, a make and/or model of the vehicle, a type of the vehicle, etc.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV. In some embodiments, imagecapture device 122 may be a 7.2M pixel image capture device with anaspect ratio of about 2:1 (e.g., H×V=3800×1900 pixels) with about 100degree horizontal FOV. Such an image capture device may be used in placeof a three image capture device configuration. Due to significant lensdistortion, the vertical FOV of such an image capture device may besignificantly less than 50 degrees in implementations in which the imagecapture device uses a radially symmetric lens. For example, such a lensmay not be radially symmetric which would allow for a vertical FOVgreater than 50 degrees with 100 degree horizontal FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

In some embodiments, one or more of the image capture devices (e.g.,image capture devices 122, 124, and 126) disclosed herein may constitutea high resolution imager and may have a resolution greater than 5Mpixel, 7M pixel, 10M pixel, or greater.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with vehicle 200. Each ofthe plurality of second and third images may be acquired as a second andthird series of image scan lines, which may be captured using a rollingshutter. Each scan line or row may have a plurality of pixels. Imagecapture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV image capture device122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). Other camera configurations are consistentwith the disclosed embodiments, and the configurations disclosed hereinare examples. For example, system 100 may include a configuration of anynumber of cameras (e.g., one, two, three, four, five, six, seven, eight,etc.) Furthermore, system 100 may include “clusters” of cameras. Forexample, a cluster of cameras (including any appropriate number ofcameras, e.g., one, four, eight, etc.) may be forward-facing relative toa vehicle, or may be facing any other direction (e.g., reward-facing,side-facing, at an angle, etc.) Accordingly, system 100 may includemultiple clusters of cameras, with each cluster oriented in a particulardirection to capture images from a particular region of a vehicle'senvironment.

The first camera may have a field of view that is greater than, lessthan, or partially overlapping with, the field of view of the secondcamera. In addition, the first camera may be connected to a first imageprocessor to perform monocular image analysis of images provided by thefirst camera, and the second camera may be connected to a second imageprocessor to perform monocular image analysis of images provided by thesecond camera. The outputs (e.g., processed information) of the firstand second image processors may be combined. In some embodiments, thesecond image processor may receive images from both the first camera andsecond camera to perform stereo analysis. In another embodiment, system100 may use a three-camera imaging system where each of the cameras hasa different field of view. Such a system may, therefore, make decisionsbased on information derived from objects located at varying distancesboth forward and to the sides of the vehicle. References to monocularimage analysis may refer to instances where image analysis is performedbased on images captured from a single point of view (e.g., from asingle camera). Stereo image analysis may refer to instances where imageanalysis is performed based on two or more images captured with one ormore variations of an image capture parameter. For example, capturedimages suitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image capture devices 122 and 126 in the aboveexample). This configuration may provide a free line of sight from thewide field of view camera. To reduce reflections, the cameras may bemounted close to the windshield of vehicle 200, and may includepolarizers on the cameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from the main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system). Furthermore, in some embodiments,redundancy and validation of received data may be supplemented based oninformation received from one more sensors (e.g., radar, lidar, acousticsensors, information received from one or more transceivers outside of avehicle, etc.).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, applications processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto applications processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. Based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar, lidar, etc.) to perform the monocular image analysis. Asdescribed in connection with FIGS. 5A-5D below, monocular image analysismodule 402 may include instructions for detecting a set of featureswithin the set of images, such as lane markings, vehicles, pedestrians,road signs, highway exit ramps, traffic lights, hazardous objects, andany other feature associated with an environment of a vehicle. Based onthe analysis, system 100 (e.g., via processing unit 110) may cause oneor more navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with determining a navigational response.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408. Furthermore, in some embodiments, stereo image analysismodule 404 may implement techniques associated with a trained system(such as a neural network or a deep neural network) or an untrainedsystem.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

Furthermore, any of the modules (e.g., modules 402, 404, and 406)disclosed herein may implement techniques associated with a trainedsystem (such as a neural network or a deep neural network) or anuntrained system.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(l), z_(l))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights. Furthermore, in some embodiments,the analysis may make use of trained system (e.g., a machine learning ordeep learning system), which may, for example, estimate a future pathahead of a current location of a vehicle based on an image captured atthe current location.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing in the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Navigating a Vehicle Based on a Detected Barrier

Consistent with disclosed embodiments, the system may determine anavigational path of a vehicle based on at least one barrier detectedfrom an image captured by an image capture device. The system maydifferentiate between traversable barriers (e.g., curb, lane marking, orroad edge, etc.) and non-traversable barriers (e.g., concrete barrier,lane dividing structure, other vehicles, tunnel wall, or bridgestructure, etc.) In an emergency situation when an obstacle (e.g., apedestrian, an animal, debris, or other non-road objects) appearsforward of the vehicle, the system may determine the navigational pathto travel through a traversable barrier in order to avoid an accident.

In addition, the system may also differentiate between movable barriers(e.g., other vehicles, traffic cones, etc.) and fixed barriers (e.g., aroad edge, a curb, a lane dividing structure, a tunnel wall, or a bridgestructure, etc.) The system may determine a position of a fixed barrier,and transmit to a remote server an update to a map for autonomousvehicle navigation based on the determined position of the fixedbarrier.

FIG. 8 illustrates a navigation system for a vehicle, consistent withdisclosed embodiments. For illustration, the vehicle is referenced asvehicle 800. The vehicle shown in FIG. 8 may be any other vehicledisclosed herein, including, for example, vehicle 200 shown in otherembodiments. As shown in FIG. 8, vehicle 800 may communicate with aremote server 850. Vehicle 800 may include at least one image capturedevice (e.g., cameras 812 and 814).

Vehicle 800 may include a navigation system 820 configured for providingnavigation guidance for vehicle 800 to travel on a road. Vehicle 800 maybe an autonomous vehicle, and navigation system 820 may be used forproviding navigation guidance for autonomous driving. Alternatively, inother embodiments, vehicle 800 may also be a non-autonomous,human-controlled vehicle, and navigation system 820 may still be usedfor providing navigation guidance.

Navigation system 820 may include a communication unit 822 configured tocommunicate with server 850 through a communication path 860. Navigationsystem 820 may include a Global Positioning System (GPS) unit 824configured to receive and process GPS signals. Navigation system 820 mayinclude at least one processor 826 configured to process data, such asGPS signals, images captured by cameras 812 and 814, and/or map datafrom server 850. Navigation system 820 may include a memory 828, whichmay store various modules that, when executed by processor 826, maycause the processor to perform various methods 828 consistent with theembodiments.

Remote server 850 may include a storage device 852 (e.g., acomputer-readable medium) provided on remote server 850 thatcommunicates with vehicle 800. Remote server 850 may store a mapdatabase 854 in storage device 852. Map database 854 may include maps ofcertain regions. Vehicle 800 may communicate with remote server 850 toreceive data included in map database 854. Vehicle 800 may alsocommunicate with remote server 850 to transmit an update of map data inmap database 854.

FIG. 9 shows an example of memory 900, consistent with disclosedembodiments. Memory 900 may be memory 828 that is included in navigationsystem 820 of vehicle 800. Memory 900 may include various modules which,when executed by a processor (e.g., processor 826), may cause theprocessor to perform various methods.

For example, memory 900 may include a navigation region determinationmodule 910. Navigation region determination module 910 may, whenexecuted by a processor, cause the processor to analyze at least one ofa plurality of images received from an image capture device (e.g., oneof cameras 812 and 814) to identify a navigable region in an environmentof a vehicle (e.g., vehicle 800).

Memory 900 may also include a barrier identification module 920. Barrieridentification module 920 may cause the processor to analyze the atleast one of the plurality of images received from the image capturedevice and identify, based on the at least one of the plurality ofimages, at least one barrier associated with an edge of the navigableregion. The at least one barrier may include a curb, a lane marking, aroad edge, a concrete barrier, a lane dividing structure, a tunnel wall,a bridge structure, etc. The barriers may include a physical object(e.g., a barrier) or a road marking, or any other indication that anarea beyond a certain edge is outside the navigable region. In oneexample, a certain physical object or road marking may be classified asa road barrier under certain circumstances or for certain vehicles, andthe same physical object or road marking may be regarded as a“non-barrier” for other vehicles or under different circumstances. Oneexample of such a dynamically shifting barrier may be a lane assignmentsign, which at certain hours during the day assigns a lane to trafficrunning in one direction, but at a different time of the day assigns thelane to traffic running in the opposite direction. The lane marking maybe a barrier (preventing vehicles to enter the lane) or an ordinary lanemarking depending on the time day and on the direction of the vehiclesmotion. In another example, a lane marking may be a barrier for vehiclesin which less than a certain number of people is travelling and, at thesame time, the same lane marking may be an ordinary lane marking forvehicles with more than a certain number of people or for publictransportation.

Barrier identification module 920 may also cause the processor todetermine a type of the at least one barrier. The type of barrier may betraversable or non-traversable. Traversable barriers may includebarriers that can be traversed by a vehicle without causing anysubstantial damage to the vehicle, the barriers, or any other objectsclose to the barriers and located on the opposite side of the barriers.Examples of the traversable barriers may include curbs, lane markings,road edges, etc. Non-traversable barriers may include barriers thatcannot be traversed by the vehicle, i.e., may cause major damage to thevehicle, the barriers, or any other objects close to the barriers.Examples of the non-traversable barriers may include concrete barriers,lane dividing structures, other vehicles, tunnel walls, or bridgestructures, etc.

Barrier identification module 920 may also be adapted to classify thearea beyond the at least one barrier, specifically when the at least onebarrier is a traversable barrier. For example, in case pedestrians aredetected beyond a shallow sidewalk curb (which can be regarded astraversable), the barrier identification module 920 may classify thearea beyond the at least one barrier as hazardous, or as potentiallyincluding pedestrians.

In some embodiments, the type of barrier may be movable or fixed.Movable barriers may include barriers whose positions are varying andnot fixed. Examples of the movable barriers may include other vehicles,traffic cones, fallen trees, or the dynamically shifting barrierdiscussed above, etc. Fixed barriers may include barriers that are partof a road or a road environment. Examples of the fixed barriers mayinclude road edges, curbs, lane dividing structures, tunnel walls, orbridge structures.

Memory 900 may also include an obstacle identification module 930.Obstacle identification module 930 may cause the processor to analyzethe at least one of the plurality of images received from the imagecapture device and identify, based on the analysis of at least one ofthe plurality of images, an obstacle forward of the vehicle. Theobstacle may be a non-road object, i.e., an object that does not belongto the road. For example, the obstacle may be a pedestrian, an animal,debris (e.g., tree, lamppost, etc.). The obstacle may be located in aposition forward of and close to the vehicle. The obstacle may belocated in a previously determined navigational path of the vehicle,such that, if the vehicle does not steer away from the navigationalpath, the vehicle might collide with the obstacle to cause a trafficaccident. In some other examples, obstacle identification module 930 maycause the processor to analyze information from radar or lidar toidentify an obstacle forward of the vehicle.

Memory 900 may further include a navigational path determination module940. Navigational path determination module 940 may cause the processorto determine a navigational path of the vehicle based on the type of theat least one barrier. For example, the processor may analyze adestination location, a current location of the vehicle, and determinethe navigational path leading the vehicle to the destination. Thenavigational path may be located in a navigable region partiallysurrounded by traversable or non-traversable barriers. When theprocessor identifies an obstacle forward of and close to the vehicle,the processor may determine the navigational path to avoid theidentified obstacle. If the identified obstacle is unavoidable, theprocessor may determine the navigational path to travel through atraversable barrier. In another example, if the identified obstacle isunavoidable, the processor may process sensor data relating to the areabeyond the barrier to determine whether or not it is safe to cross thetraversable barrier, and may possibly also compute a safe or even thesafest path which include at least an area that is beyond thetraversable barrier.

Memory 900 may further include a map update module 950. Map updatemodule 950 may cause the processor to determine whether to transmit to aremote server (e.g., remote server 850) an update to a map (e.g., mapdatabase 854) based on the determined type of the barrier. When thedetermined type of the barrier is movable, the processor may determinenot to transmit an update. Otherwise, when the determined type of thebarrier is fixed, the processor may determine to transmit a map updaterelated to the fixed barrier to the remote processor. The map update mayinclude a position, shape, size, and/or identifier of the fixed barrier,and possibly also data about the area beyond the barrier (for example,whether or not a hazard, such as pedestrians, for example, wasidentified in the area beyond the barrier).

FIG. 10A schematically illustrates a bird's eye view of an environment1000 of an exemplary vehicle 1010 consistent with disclosed embodiments.The exemplary vehicle 1010 may be, for example, vehicle 800 describedabove in reference to FIG. 8 and may include a navigation system, suchas navigation system 820 of vehicle 800.

As shown in FIG. 10A, environment 1000 of vehicle 1010 includes a roadarea 1020 defined by a concrete barrier 1022 and a curb 1024, and anon-road area 1030 defined by curb 1024 and a concrete barrier 1026.Road area 1020 includes a left lane 1020A and a right lane 1020B dividedby a lane marking 1028. Vehicle 1010 is traveling on right lane 1020B.Another vehicle (e.g., a truck) 1015 is traveling on right lane 1020Bforward of vehicle 1010. A plurality of vehicles 1011, 1012, 1013, and1014 are traveling on left lane 1020A. A pedestrian 1018 is walking onright lane 1020B.

FIG. 10B schematically illustrates an image 1040 of environment 1000captured by a forward facing image capture device on exemplary vehicle1010. The forward facing image capture device may be, for example, oneof cameras 812 and 814 of vehicle 800. A processor (e.g., processor 826included in navigation system 820 of vehicle 800) may analyze image 1040to determine a navigable region 1050 with an edge 1060. Edge 1060 may bedisposed along sides of vehicles 1011-1014 close to vehicle 1010 (i.e.,viewer of image 1040), a rear side of vehicle 1015, and curb 1024.Barriers (e.g., vehicles 1011-1015 and curb 1024) associated with atleast one portion of edge 1060 may be used to determine a navigationalpath 1070 for vehicle 1010.

FIG. 11 is a flowchart showing an exemplary process 1100 for navigatinga vehicle based on detected barriers, consistent with disclosedembodiments. Process 1100 may be performed by a processor (e.g.,processor 826) onboard of a vehicle (e.g., vehicle 800). Process 1100may analyze at least one image taken by a forward facing image capturedevice (e.g., one of cameras 812 and 814) to determine a navigationalpath for the vehicle.

At step 1110, processor 826 may receive, from an image capture device, aplurality of images associated with an environment of vehicle 800. Image1040 shown in FIG. 10B is an example of an image of environment 1000 ofvehicle 800 that may be received from an image capture device (e.g., oneof cameras 812 and 814) on vehicle 800. In some embodiments, theplurality of images may be captured at different times by the imagecapture device (e.g., images may be captured apart by less than asecond, 1 second, 2 second, etc.). In some embodiments, vehicle 800 mayinclude a plurality of image capture devices (e.g., cameras 812 and814), and processor 826 may receive from each image capture device, aplurality of images associated with environment 1000 of vehicle 800. Theplurality of images received from each image capture device may beimages captured at different times by each image capture device.

At step 1120, processor 826 may analyze at least one of the plurality ofimages received from the image capture device. In embodiments where asingle plurality of images is generated based on images received from aplurality of image capture devices, processor 826 may analyze at leastone image of the single plurality of images. Alternatively, each imagereceived from each image capture device may be analyzed independently.

Processor 826 may also identify, based on the analysis of the at leastone of the plurality of images, a navigable region in the environment ofvehicle 800. For example, processor 826 may identify, based on image1040, a navigable region 1050 with an edge 1060.

In one embodiment, processor 826 may determine the navigable region byusing Convolutional Neural Networks (CNN). For example, processor 826may be trained by using a plurality of training images that weremanually labeled with free space pixels and non-free space pixels. As aresult of the training, processor 826 may label the pixels in image 1040as free space pixels and non-free space pixels. Processor 826 may thendetermine a boundary between the free space pixels and non-free spacepixels. Processor 826 may identify a region surrounded, or partiallysurrounded, by the boundary as the navigable region.

At step 1130, processor 826 may identify at least one barrier associatedwith an edge of the navigable region. The at least one barrier mayinclude curb, lane marking, road edge, concrete barrier, lane dividingstructure, tunnel wall, or bridge structure, etc. For example, processor826 may identify vehicles 1011-1015 and curb 1024 as barriers associatedwith associated with edge 1060 of navigable region 1050.

In one embodiment, a memory (e.g., memory 828) may store a plurality oftraining images of various barriers. Processor 826 may compare image1040 with the plurality of training images to identify objects in image1040 that have at least one feature (e.g., shape, color, etc.) thatmatch the features of the barriers in the training images. Processor 826may determine the identified objects as the barriers.

In some embodiments, each of the barriers in the training images may beassociated with a barrier identifier. Examples of barrier identifiersmay include “vehicle,” “lane marking,” “road edge,” “concrete barrier,”“lane dividing structure,” “tunnel wall,” or “bridge structure,” etc.When processor 826 identifies an object that matches a barrier in thetraining images, processor 826 may label the identified object with thebarrier identifier associated with the matching barrier.

At step 1140, processor 826 may determine a type of the at least onebarrier that was identified at step 1130. The type of barriers may betraversable or non-traversable. Examples of the traversable barriers mayinclude curbs, lane markings, or road edges, etc. Examples of thenon-traversable barriers may include concrete barriers, lane dividingstructures, other vehicles, tunnel walls, or bridge structures, etc. Inimage 1040 shown in FIG. 10B, processor 826 may identify vehicles1011-1015 as non-traversable barriers, and may identify curb 1024 as atraversable barrier. The type of barriers may also be movable or fixed.Examples of the movable barriers may include other vehicles, trafficcones, fallen trees, etc. Examples of fixed barriers may include roadedges, curbs, lane dividing structures, tunnel walls, or bridgestructures. In image 1040 shown in FIG. 10B, processor 826 may identifyvehicles 1011-1015 as movable barriers, and may identify curb 1024 as afixed barrier.

In one embodiment, a memory (e.g., memory 828 of vehicle 800) may storea database including a plurality of barrier identifiers eachcorresponding to one or more barrier types. When processor 826identifies a barrier in an image and labels the barrier with a barrieridentifier, processor 826 may refer to the database to identify one ormore barrier types that correspond to the barrier identifier. Theidentified barrier types may be considered by processor 825 as thebarrier type of the barrier identified in the image.

In another embodiment for determining a barrier type, processor 826 maybe trained by a plurality of training images that were manually labeledwith free space pixels, non-free space pixels, and various types ofbarrier pixels that respectively correspond to various barriers. As aresult of training by using these training images, processor 826 maylabel the pixels in image 1040 as free space pixels, non-free spacepixels, and various types of barrier pixels. Signatures that weregenerated by other types of sensors, such as LiDar, Radar or Ultrasonicsensors, can also be used for training the processor 826, and thetrained processor 826 may be enabled, based on inputs from correspondingsensing units, to label free space pixels, non-free space pixels, andvarious types of barrier pixels. The data from different types ofsensors may be fused and the sensor 826 can be configured to label freespace pixels, non-free space pixels, and various types of barrier pixelsbased on inputs from different types of sensors.

At step 1150, processor 826 may identify, based on the analysis of theat least one image, an obstacle forward of vehicle 800. The obstacle maybe a non-road object, i.e., an object that does not belong to the road.The obstacle may be located in a position forward of and close to thevehicle. For example, the obstacle may be a pedestrian, an animal, afallen object (e.g., tree, lamppost, etc.). In image 1040, processor 826may identify pedestrian 1018 as an obstacle.

In one embodiment, a memory (e.g., memory 828) may store a plurality oftraining images of various obstacles. Processor 826 may compare image1040 with the plurality of training images to identify an object inimage 1040 that has at least one feature (e.g., shape, color, etc.) thatmatches a feature of an obstacle in the training images. Processor 826may determine the identified objects as the obstacle. Alternatively, theplurality of training images of various obstacles may be used to createa trained system, such as a neural network or a deep neural network forexample, and the trained system may be used by the processor 826 todetect an obstacle in the image 1040 or to determine if an object in theimage 1040 is an obstacle, and possibly also what type of obstacle itis.

Processor 826 may also determine, based on the analysis of the at leastone image, a position of the obstacle relative to the vehicle. Forexample, processor 826 may determine the position of the obstacle basedon a size of the obstacle as it appears in the image 1040, a typicalsize of that type of obstacle, and/or a relative position between theobstacle and the barriers along the road.

Processor 826 may further determine, based on the position of theobstacle, whether the obstacle is located in a previously determinednavigational path of the vehicle and whether, if the vehicle does notsteer away from the previously determined navigational path, the vehiclemight collide with the obstacle to cause a traffic accident. In theexample of FIG. 10B, pedestrian 1018 is located at a previouslydetermined navigational path 1080, and the distance between pedestrian1018 and vehicle 1010 is less than a threshold distance value such thatvehicle 1010 might hit pedestrian if it does not steer away from thepreviously determined navigational path 1080.

At step 1160, processor 826 may determine a navigational path of thevehicle based on the identified obstacle and the determined type of theobstacle. For example, processor 826 may determine a navigational paththrough the navigable region to avoid the obstacle. If such anavigational path is not available through the navigable region,processor 826 may analyze the type of the barrier associated with theedge of the navigable region. If the barrier is traversable, processor826 may determine the navigational path to travel through thetraversable barrier. In the example of FIG. 10B, processor 826 maydetermine a new navigational path 1070 that travels over curb 1024,which is a traversable barrier, in order to avoid pedestrian 1018.

At step 1170, processor 826 may cause the vehicle to travel on at leasta portion of the determined navigational path. In some embodiments,processor 826 may cause one or more navigational responses in vehicle800 to navigate along the determined navigational path. Navigationalresponses may include, for example, a turn, a lane shift, a change inacceleration, and the like. Additionally, multiple navigationalresponses may occur simultaneously, in sequence, or any combinationthereof to navigate along the determined navigational path. Forinstance, processor 826 may cause vehicle 800 to move laterally and thenaccelerate by, for example, sequentially transmitting control signals toa steering system (e.g., steering system 240) and a throttling system(e.g., throttling system 220). Alternatively, processor 826 may causevehicle 800 to brake while at the same time moving laterally, forexample, simultaneously transmitting control signals to a braking system(e.g., braking system 230) and a steering system (e.g., steering system240).

FIG. 12 is a flowchart showing an exemplary process 1200, consistentwith disclosed embodiments. Process 1200 may be performed by a processor(e.g., processor 826) onboard of a vehicle (e.g., vehicle 800). Process1100 may analyze at least one image taken by a forward facing imagecapture device (e.g., one of cameras 812 and 814) to obtain informationfor updating a map.

At step 1210, processor 826 may receive, from an image capture device, aplurality of images associated with an environment of vehicle 800. Atstep 1220, processor 826 may analyze at least one of the plurality ofimages received from the image capture device to identify a navigableregion in the environment of vehicle 800. At step 1230, processor 826may identify at least one barrier associated with an edge of thenavigable region. At step 1240, processor 826 may determine a type ofthe at least one barrier. Steps 1210 through 1240 are substantially thesame as steps 1110 through 1140 of process 1100. Therefore, detaileddescription of steps 1210 through 1240 are not repeated here.

After determining the type of the at least one barrier, processor 826may transmit to a remote server (e.g., server 850) an update to a map ina map database (e.g., map database 854) for autonomous vehiclenavigation based on the determined type of barrier. Specifically, atstep 1250, processor 826 may determine whether the barrier is fixed. Asdiscussed above, fixed barriers may include barriers that are part of aroad or road environment. Examples of fixed barriers may include roadedges, curbs, lane dividing structures, tunnel walls, or bridgestructures. In the example of FIG. 10B, processor 826 may determine curb1024 as a fixed barrier.

If the barrier is fixed (step 1250: Yes), then, at step 1260, processor826 may transmit to the remote server an update to a map (i.e., mapupdate) related to the fixed barrier. The map update may include abarrier identifier, a barrier type, and a location of the fixed barrier.

For example, processor 826 may use the following method to determine thelocation of the fixed barrier. First, processor 826 may acquire alocation of vehicle 800 based on GPS data received by a GPS unit (e.g.,GPS unit 824). Processor 826 may also analyze the at least one image todetermine a relative location of the fixed barrier with respect tovehicle 800. Processor 826 may then determine the location of the fixedbarrier based on the location of vehicle 800 and the relative locationof the fixed barrier with respect to vehicle 800.

If the barrier is movable (step 1250: No), processor 826 may decide notto update the map. Instead, processor 826 may finish process 1200. Oncethe map is updated, the map may be used by a navigation system onvehicle 800 or other vehicle for autonomous navigation.

FIG. 13 is a flowchart showing an exemplary process 1300 for navigatinga vehicle based on detected barriers, consistent with disclosedembodiments. Process 1300 may be performed by a processor (e.g.,processor 826) onboard of a vehicle (e.g., vehicle 800). Process 1300may analyze at least one image taken by a forward facing image capturedevice (e.g., one of cameras 812 and 814) to determine a navigationalpath for the vehicle.

At step 1310, processor 826 may receive, from an image capture device, aplurality of images associated with an environment of vehicle 800. Atstep 1320, processor 826 may analyze at least one of the plurality ofimages received from the image capture device. Steps 1310 and 1320 aresubstantially the same as steps 1110 and 1120 of process 1100.Therefore, detailed description of steps 1310 and 1320 are not repeatedhere.

At step 1330, processor 826 may identify a first barrier associated witha first portion of an edge of the navigable region and a second barrierassociated with a second portion of the edge of the navigable region.The first barrier may be a road edge or a curb. The second barrier maybe a lane dividing structure, another vehicle, a tunnel wall, or abridge structure. In the example of FIG. 10B, processor 826 may identifycurb 1024 associated with a right-side portion of edge 1060 of navigableregion 1050 as the first barrier. Processor 826 may identify vehicle1015 associated with a forward-side portion of edge 1060 as the secondbarrier.

At step 1340, processor 826 may determine a type of the first barrierand a type of the second barrier. In the example of FIG. 10B, processor826 may identify curb 1024 as a traversable barrier, and vehicle 1015 asa non-traversable barrier.

At step 1350, processor 826 may determine a navigational path of thevehicle based on the type of the first barrier and the type of thesecond barrier. In the example of FIG. 10B, processor 826 may determinenavigational path 1070 that travels over curb 1024, which istraversable, in order to avoid vehicle 1015, which is non-traversable.

At step 1360, processor 826 may cause the vehicle to travel on at leasta portion of the determined navigational path. In the example of FIG.10B, processor 826 may cause vehicle 1010 to travel on at least aportion of navigational path 1070.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray,or other optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A vehicle navigation system for a vehicle, thevehicle navigation system comprising: a memory including instructions;and one or more processors that, when in operation, are configured bythe instructions to: estimate, by use of a machine learning system, afuture path of the vehicle ahead of a current location of the vehicle;analyze at least one of a plurality of images to identify a lane of thevehicle in which the vehicle travels, the plurality of images capturedby a camera of the vehicle; identify, based on the at least one of theplurality of images, a lane marking separating a current lane of thevehicle and an adjacent lane; identify a candidate vehicle forward ofthe vehicle in the lane in which the vehicle travels; and cause thevehicle to traverse the lane marking to change to the adjacent lane inresponse to identifying the candidate vehicle as an obstacle forward ofthe vehicle.
 2. The vehicle navigation system of claim 1, wherein, toidentify the candidate vehicle, the instructions configure the one ormore processors to use radar.
 3. The vehicle navigation system of claim1, wherein the candidate vehicle is moving slower than the vehicle. 4.The vehicle navigation system of claim 1, wherein the instructionsconfigure the one or more processors to traverse the lane marking tochange to the adjacent lane without input from a driver of the vehicle.5. The vehicle navigation system of claim 1, wherein, to cause thevehicle to traverse the lane marking to change to the adjacent lane, theinstructions configure the one or more processors to make a change inacceleration.
 6. The vehicle navigation system of claim 5, wherein thechange in acceleration increases the acceleration.
 7. A non-transitorymachine-readable medium including instructions that, when executed bycircuitry of a vehicle component, cause the circuitry to performoperations comprising: estimating, by use of a machine learning system,a future path of the vehicle ahead of a current location of the vehicle;analyzing at least one of a plurality of images to identify a lane ofthe vehicle in which the vehicle travels, the plurality of imagescaptured by a camera of the vehicle; identifying, based on the at leastone of the plurality of images, a lane marking separating a current laneof the vehicle and an adjacent lane; identifying a candidate vehicleforward of the vehicle in the lane in which the vehicle travels; andcausing the vehicle to traverse the lane marking to change to theadjacent lane in response to identifying the candidate vehicle as anobstacle forward of the vehicle.
 8. The machine-readable medium of claim7, wherein identifying the candidate vehicle includes using radar. 9.The machine-readable medium of claim 7, wherein the candidate vehicle ismoving slower than the vehicle.
 10. The machine-readable medium of claim7, wherein causing the vehicle to traverse the lane marking to change tothe adjacent lane is performed without input from a driver of thevehicle.
 11. The machine-readable medium of claim 7, wherein causing thevehicle to traverse the lane marking to change to the adjacent laneincludes making a change in acceleration.
 12. The machine-readablemedium of claim 11, wherein the change in acceleration increases theacceleration.
 13. A vehicle comprising: a camera; and a vehiclenavigation system including a processor, the vehicle navigation systemconfigured to: estimate, by use of a machine learning system, a futurepath of the vehicle ahead of a current location of the vehicle; analyzeat least one of a plurality of images to identify a lane of the vehiclein which the vehicle travels, the plurality of images captured by thecamera; identify, based on the at least one of the plurality of images,a lane marking separating a current lane of the vehicle and an adjacentlane; identify a candidate vehicle forward of the vehicle in the lane inwhich the vehicle travels; and cause the vehicle to traverse the lanemarking to change to the adjacent lane in response to identifying thecandidate vehicle as an obstacle forward of the vehicle.
 14. The vehicleof claim 13, wherein, to identify the candidate vehicle, the vehiclenavigation system is configured to use radar.
 15. The vehicle of claim13, wherein the candidate vehicle is moving slower than the vehicle. 16.The vehicle of claim 13, wherein the vehicle navigation system isconfigured to traverse the lane marking to change to the adjacent lanewithout input from a driver of the vehicle.
 17. The vehicle of claim 13,wherein, to cause the vehicle to traverse the lane marking to change tothe adjacent lane, the vehicle navigation system is configured to make achange in acceleration.
 18. The vehicle of claim 17, wherein the changein acceleration increases the acceleration.